New Tool for Counter-Drone Technology Developers
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Small, low flying, non-cooperative drones is can be detected and identified by radars. A database of drone radar cross-sections (RCS) has been made public to help those developing countermeasures better understand how drones can be detected, and to investigate the difference between drone models and materials in terms of scattering radio signals.
RCS is a measure of how detectable an object is by radar. When electromagnetic waves are transmitted towards an object, the object reflects a limited amount of radar energy back to the source. RCS data is critical in the development of radars and stealth systems, especially stealth aircraft. A platform’s RCS is usually kept secret.
Attend AUS&R Conference and Exhibition on September 6, 2020
The dataset contains measurement results of RCS of different UAVs at 26-40 GHz. It shows how radio waves are scattered by different UAVs at the specified frequency range.
The researchers believe that their results will be a starting point for a future uniform drone database. It is hoped that, alongside assisting with the development of radar, the data will enable machine learning algorithms to make more subtle identifications – towards this, more measurements are to be added.
“There is an urgent need to find better ways to monitor drone use,” said researcher
Vasilii Semkin of VTT Technical Research Centre of Finland. “We aim to continue this work and extend the measurement campaign to other frequency bands, as well as for a larger variety of drones and different real-life environments.”
mmW radar is considered the best candidate for drone detection, but 5G base stations may offer an alternative. “We are developing millimetre-wave wireless communication technology, which could also be used in sensing the environment like a radar. With this technology, 5G-base stations could detect drones, among other things,” according to Professor Ville Viikari of Aalto University in Finland.
Nine drones were measured, as well as a lithium-ion polymer battery. As expected, according to the team, larger drones made of carbon fibre proved easier to detect, whereas drones made from plastic and foam were less visible, according to electronicsweekly.com.
The research paper Analyzing radar cross section signatures of diverse drone models at mmWave frequencies was published by the IEEE.
Interested in learning more about unmanned systems and counter-measures? Attend the AUS&R Unmanned Systems and Robotics Conference and Exhibition on September 6, 2020 at the Lago Conference Center, Rishon LeZion.
